How is XPndAI's AI agent evaluation different from internal testing?

Internal testing by the build team typically checks that the agent works — it passes the happy path. Professional evaluation tests whether the agent is production-ready at scale: (1) We build adversarial test cases based on real failure modes in your domain, not just the expected inputs; (2) We establish a benchmark dataset with ground-truth answers so accuracy is measured, not just observed; (3) We use domain-expert evaluators (e.g., a Chartered Accountant evaluating a CA AI agent, a Dubai real estate agent evaluating a property voice agent) whose judgment cannot be automated; (4) We measure failure modes in production — conversations the agent handled poorly — and add them as new test cases; (5) We give you a score that tracks over time so you can measure improvement or regression with each model update.

AI Agent Evaluation · Testing · QA

AI Agent
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Q: What does AI agent evaluation involve?

AI agent evaluation is the systematic process of measuring whether your AI agent performs correctly, reliably, and safely. It covers: (1) Functional correctness — does the agent complete the tasks it was designed for? (2) Accuracy — are the agent's outputs factually correct? (3) Hallucination rate — how often does the agent fabricate information? (4) Task completion rate — what percentage of user intents does the agent fulfill without human fallback? (5) Safety and guardrails — does the agent refuse harmful requests correctly? (6) Latency and reliability — does it meet response time SLAs under production load? (7) Domain-specific accuracy — for a real estate agent: are property prices, availability, and specifications correct? For a collections agent: are account balances, PTP dates, and payment links accurate? Evaluation should run before production launch, after model updates, and continuously in production to catch regressions.

Your AI agent works in demos. The question is whether it works at production scale, with real users, adversarial inputs, and domain-specific accuracy requirements. We evaluate it — before and after launch — so you know exactly what it gets right and wrong.

Benchmark Datasets Hallucination Testing Task Completion Rate Human Evaluation Production Monitoring Model Comparison

Get Agent Evaluated 8-Phase Methodology

Free Evaluation Scoping — 30 min

Tell us your agent type and what failure modes concern you most. We'll scope a benchmark in 30 minutes.

Evaluation Methodology

8-Phase AI Agent Evaluation

From initial audit to continuous production monitoring — a structured methodology that turns evaluation into an ongoing quality system, not a one-time checkbox.

Phase 1

Evaluation Audit

Map every task the agent is supposed to do. Define success criteria per task. Identify the highest-risk failure modes for your specific domain and user base.

Foundation

Phase 2

Benchmark Dataset

Build a ground-truth dataset of 200–500 representative inputs with correct expected outputs. Adversarial inputs included — edge cases that break most agents.

Measurement

Phase 3

Human Evaluation

Domain experts rate agent outputs on accuracy, helpfulness, tone, and safety. A CA evaluates a finance agent. A Dubai broker evaluates a real estate voice agent.

Quality

Phase 4

Continuous Production Monitoring

Live production conversations sampled and evaluated automatically. Anomaly detection flags conversations where confidence or output quality drops below threshold.

Continuous

Phase 5

Failed Conversations → Test Cases

Every conversation the agent handles poorly becomes a new test case. Benchmark grows with production experience — catching regression before users do.

Continuous

Phase 6

Model / Agent Comparison

When you update the LLM, prompt, or agent logic — A/B evaluation against the benchmark. Evidence-based decisions, not gut feel about model upgrades.

Governance

Phase 7

Domain-Expert Evaluation

Quarterly deep-dive evaluation by domain experts who understand the nuance your automated metrics can't catch: tone, regulatory compliance, correct promises.

Governance

Phase 8

XPndAI Evaluation API

For teams running evaluation at scale — an API that scores any agent output against your benchmark automatically. Integrate into CI/CD to catch regressions before deploy.

Platform

What We Measure

Evaluation Metrics — All Agent Types

Task Completion Rate

% of intents fully resolved

Hallucination Rate

Fabricated facts per 100 outputs

Factual Accuracy

Correct vs ground truth

Safety Score

Harmful / non-compliant outputs

Fallback Rate

Human escalation triggers

Latency P95

95th percentile response time

Coherence

Logical, consistent responses

Groundedness

Answers supported by context

Intent Recognition

Correct intent classified

Instruction Following

Guardrail + system prompt compliance

Regression Score

Performance vs previous version

Domain Accuracy

Vertical-specific correct answers

Evaluation by Agent Type

Domain-Specific Evaluation

🏠

Real Estate Voice Agent (Dubai)

Evaluation specific to property AI:

Correct property price + availability?
Correct lead qualification (buyer intent)?
CRM updated with right fields?
Viewing booked at correct time?
Arabic ↔ English switching correct?
Wrong promises to customer? (price guarantees, etc.)
Correct broker assigned per lead type?

💰

Collections Voice Agent (NBFC)

Evaluation specific to debt recovery:

Correct account balance stated?
Promise-to-pay date recorded accurately?
Payment link sent to correct number?
Correct loan product identified?
RBI compliance — no prohibited language?
Correct human escalation triggers?
DPD bucket handled with right script?

📄

Document AI Agent

Evaluation specific to document processing:

Extraction accuracy per field type
Table extraction vs nested data
Handwritten vs printed accuracy delta
Multi-language document accuracy
Edge case: damaged / low-res docs
False positive rate on structured fields
ERP posting accuracy after extraction

🧾

CA / Finance Agent (India)

Evaluation specific to CA practice AI:

GSTR-2B matching accuracy vs ground truth
TDS credit identification rate
ITR schedule pre-fill accuracy
ROC deadline calculation correctness
ICAI compliance — CA sign-off respected?
WhatsApp bot — correct client-specific answers?

🎧

Customer Support Agent

Evaluation specific to support AI:

Issue resolution rate (no human needed)
Escalation precision (right issues escalated)
Policy compliance (correct T&C answers)
Tone and empathy score
Repeat contact rate after AI resolution
Hallucinated product features / pricing

📊

RAG Knowledge Base Agent

Evaluation specific to RAG systems:

Retrieval recall — right chunks retrieved?
Answer faithfulness to source docs
Hallucination: answers not in corpus
Multi-hop reasoning accuracy
Stale document detection
Full RAG evaluation: see our dedicated page

Evaluation Cluster

Full AI Evaluation Stack

LLM Evaluation ServicesHallucination + accuracy → RAG Evaluation ServicesRetrieval + faithfulness → Voice AI TestingSpeech + conversation → AI Agent DevelopmentWe build it too → Custom Voice AIBuild + evaluate → Enterprise AI AgentsMulti-agent →

Pricing

AI Agent Evaluation Pricing

From one-time pre-launch audit to continuous production monitoring. Fixed scope, clear deliverables.

Pre-Launch Audit

₹2L – ₹8L

One-time evaluation before go-live

Phases 1–3: audit + benchmark + human eval
200-case benchmark dataset (ground truth)
Hallucination rate measurement
Task completion rate report
Domain-expert evaluation (1 round)
Detailed findings + fix recommendations
2–3 week delivery

Get Pre-Launch Audit

Common Questions

What does AI agent evaluation involve?

AI agent evaluation measures whether your agent performs correctly, reliably, and safely across all real-world inputs — not just demo cases. It covers functional correctness, factual accuracy, hallucination rate, task completion rate, safety and guardrail compliance, latency, and domain-specific accuracy. Evaluation should run before production launch, after every model or prompt update, and continuously in production to catch regressions before users notice them.

How is XPndAI's evaluation different from internal testing?

Internal testing checks that the agent works on the expected inputs. Professional evaluation builds adversarial test cases from real failure modes, establishes ground-truth benchmarks so accuracy is measured not just observed, uses domain experts whose judgment automated metrics can't replace, and feeds production failures back into the test set. The result is a score that tracks over time — so you can measure improvement or regression with every model update.

Can XPndAI evaluate an agent built by another company?

Yes. We evaluate AI agents regardless of who built them. We need access to the agent (API, staging environment, or conversation logs), the task specification (what the agent is supposed to do), and ideally a sample of real production conversations. We build the benchmark, run the evaluation, and deliver findings with specific fix recommendations — which can be acted on by your existing team or by us.

AI AgentEvaluationServices